Abstract—In general, the optimization problems arise in real
life situations are constrained in nature. Handling such
problems becomes more difficult when an optimization problem
involves some equality constraints. The computation complexity increases exponentially if the constraints are non-linear and non-convex. In order to solve such problems, a large number of
evolutionary approaches have been suggested in the literature. In such approaches, penalty function method and boundary simulation method are more popular to solve equality and inequality constraints problems. In this paper, an efficient and novel approach namely ‘Drosophila Food-Search Constrained
Optimization Algorithm (DFCOA)’ has been proposed that
helps for the robust generation of feasible regions during
simulation. The proposed algorithm is initially tested on ten
typical constrained benchmark problems with different tastes.
Further two real life engineering problems have also been
solved. The simulation results confirm that the proposed
algorithm works better than some of the state-of-the-art
algorithms.

Index Terms—Tournament selection, mQA, redundant
search, GPCR.

The authors are with the Department of Mathematics, NIT Silchar, Assam,
India (e-mail: kedar.iitr@gmail.com, tksingh1977.nits@gmail.com).